code: def std_of_the_standardized_error(
      y_obs: np.ndarray,
      y_pred: np.ndarray,
      se_pred: np.ndarray,
  ) -> float:
      """Standard deviation of the error standardized by the predictive standard deviation
      of the model `se_pred`. If the uncertainty is quantified well, should be close to 1.
  
      NOTE: This assumes that `se_pred` is the predictive standard deviation of the
      *observations* of the objective `y`, not the predictive standard deviation of the
      objective `f` itself. In practice, this will matter for very noisy observations.
  
      Args:
          y_obs: An array of observations for a single metric.
          y_pred: An array of the predicted values corresponding to y_obs.
          se_pred: An array of the standard errors of the predicted values.
  
      Returns:
          The scalar standard deviation of the standardized error.
      """
      return ((y_obs - y_pred) / se_pred).std()
  
file_path: Ax/ax/utils/stats/model_fit_stats.py
imports: from typing import Dict, Mapping, Optional, Protocol
  import numpy
  from scipy.stats import fisher_exact, norm, pearsonr, spearmanr
label_desc: Use image node_id 4 for calling a global function with example usage: std_of_the_standardized_error(y_obs, y_pred, se_pred) and returns: unknown